[法语学习]14Estimating Standard Errors in Finance Panel Data Sets-Comparing Approaches.pdf

Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches Mitchell A. Petersen Northwestern University In corporate finance and asset pricing empirical work, researchers are often confronted with panel data. In these data sets, the residuals may be correlated across firms or across time, and OLS standard errors can be biased. Historically, researchers in the two literatures have used different solutions to this problem. This paper examines the different methods used in the literature and explains when the different methods yield the same (and correct) standard errors and when they diverge. The intent is to provide intuition as to why the different approaches sometimes give different answers and give researchers guidance for their use. (JEL G12, G3, C01, C15) It is well known that OLS standard errors are unbiased when the residuals are in- dependent and identically distributed. When the residuals are correlated across observations, OLS standard errors can be biased and either over or underesti- mate the true variability of the coefficient estimates. Although the use of panel data sets (e.g., data sets that contain observations on multiple firms in multiple years) is common in finance, the ways that researchers have addressed possible biases in the standard errors varies widely and in many cases is incorrect. In recently published finance papers, which include a regression on panel data, 42% of the papers did not adjust the standard errors for possible dependence in the residuals.1 Approaches for estimating the coefficients and standard errors in the presence of the within-cluster correlation varied among the remaining I thank the Center for Financial Institutions and Markets at Northwestern University’s Kellogg School for support. In writing this paper, I have benefited greatly from discussions with John Ammer, Robert Chirinko, To